arXiv (All)
open access
MMKG-R1: Constructing Multimodal Knowledge Graphs via Reinforcement Learning
Multimodal knowledge graphs (MMKGs) organize information across different modalities to support multimodal Graph Retrieval-Augmented Generation (RAG), which aims to improve the performance of multimodal large language models (MLLMs). The effectiveness of these multimodal Graph RAG methods largely de
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion#987775
- No-Free-Graph: Learning When Multimodal Data Should Be Graphified#1187082
- Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs#998882
- Multimodal Cultural Heritage Knowledge Graph Extension with Language and Vision Models#257410
- LLaTA: Unlocking Graph Structure Learning with Tree-Guided Large Language Models#1233684
Record · ID 1345164
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive